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aGrUM 2.3.2
a C++ library for (probabilistic) graphical models
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aGrUM 2.3.2
a C++ library for (probabilistic) graphical models
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This is the complete list of members for gum::learning::ScoreLog2Likelihood, including all inherited members.
| cache_ | gum::learning::Score | protected |
| clear() | gum::learning::Score | |
| clearCache() | gum::learning::Score | |
| clearRanges() | gum::learning::Score | |
| clone() const | gum::learning::ScoreLog2Likelihood | virtual |
| counter_ | gum::learning::Score | protected |
| database() const | gum::learning::Score | |
| empty_ids_ | gum::learning::Score | protected |
| getNumberOfThreads() const | gum::learning::Score | virtual |
| internalPrior() const final | gum::learning::ScoreLog2Likelihood | virtual |
| isGumNumberOfThreadsOverriden() const | gum::learning::Score | virtual |
| isPriorCompatible() const final | gum::learning::ScoreLog2Likelihood | virtual |
| isPriorCompatible(PriorType prior_type, double weight=1.0f) | gum::learning::ScoreLog2Likelihood | static |
| isPriorCompatible(const Prior &prior) | gum::learning::ScoreLog2Likelihood | static |
| isUsingCache() const | gum::learning::Score | |
| marginalize_(const NodeId X_id, const std::vector< double > &N_xyz) const | gum::learning::Score | protected |
| minNbRowsPerThread() const | gum::learning::Score | virtual |
| nodeId2Columns() const | gum::learning::Score | |
| one_log2_ | gum::learning::Score | protected |
| operator=(const ScoreLog2Likelihood &from) | gum::learning::ScoreLog2Likelihood | |
| operator=(ScoreLog2Likelihood &&from) | gum::learning::ScoreLog2Likelihood | |
| gum::learning::Score::operator=(const Score &from) | gum::learning::Score | protected |
| gum::learning::Score::operator=(Score &&from) | gum::learning::Score | protected |
| prior_ | gum::learning::Score | protected |
| ranges() const | gum::learning::Score | |
| Score(const DBRowGeneratorParser &parser, const Prior &external_prior, const std::vector< std::pair< std::size_t, std::size_t > > &ranges, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >()) | gum::learning::Score | |
| Score(const DBRowGeneratorParser &parser, const Prior &external_prior, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >()) | gum::learning::Score | |
| Score(const Score &from) | gum::learning::Score | protected |
| Score(Score &&from) | gum::learning::Score | protected |
| score(const IdCondSet &idset) | gum::learning::ScoreLog2Likelihood | |
| score(const NodeId var) | gum::learning::ScoreLog2Likelihood | |
| score(const NodeId var, const std::vector< NodeId > &rhs_ids) | gum::learning::ScoreLog2Likelihood | |
| score_(const IdCondSet &idset) final | gum::learning::ScoreLog2Likelihood | protectedvirtual |
| ScoreLog2Likelihood(const DBRowGeneratorParser &parser, const Prior &prior, const std::vector< std::pair< std::size_t, std::size_t > > &ranges, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >()) | gum::learning::ScoreLog2Likelihood | |
| ScoreLog2Likelihood(const DBRowGeneratorParser &parser, const Prior &prior, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >()) | gum::learning::ScoreLog2Likelihood | |
| ScoreLog2Likelihood(const ScoreLog2Likelihood &from) | gum::learning::ScoreLog2Likelihood | |
| ScoreLog2Likelihood(ScoreLog2Likelihood &&from) | gum::learning::ScoreLog2Likelihood | |
| setMinNbRowsPerThread(const std::size_t nb) const | gum::learning::Score | virtual |
| setNumberOfThreads(Size nb) | gum::learning::Score | virtual |
| setRanges(const std::vector< std::pair< std::size_t, std::size_t > > &new_ranges) | gum::learning::Score | |
| use_cache_ | gum::learning::Score | protected |
| useCache(const bool on_off) | gum::learning::Score | |
| ~Score() | gum::learning::Score | virtual |
| ~ScoreLog2Likelihood() | gum::learning::ScoreLog2Likelihood | virtual |